Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Association Rules Mining for Name Entity Recognition
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
University of Durham: description of the LOLITA system as used in MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
Wayne State University: description of the UNO natural language processing system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
International Journal of Business Intelligence and Data Mining
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We present a novel named entity recognition approach for the Indonesian language. We call the new method InNER for Indonesian Named Entity Recognition. InNER is based on a set of rules capturing the contextual, morphological, and part of speech knowledge necessary in the process of recognizing named entities in Indonesian texts. The InNER strategy is one of knowledge engineering: the domain and language specific rules are designed by expert knowledge engineers. After showing in our previous work that mined association rules can effectively recognize named entities and outperform maximum entropy methods, we needed to evaluate the potential for improvement to the rule based approach when expert crafted knowledge is used. The results are conclusive: the InNER method yields recall and precision of up to 63.43% and 71.84%, respectively. Thus, it significantly outperforms not only maximum entropy methods but also the association rule based method we had previously designed.